Compute all stopping boundaries for a two-analysis group sequential design

Wrapper function that computes the stopping boundaries for both the first and second analyses of a group sequential design with multiple treatment regimes. Calls get_first_bound and get_next_bound sequentially.

Usage

get_bounds(
  alpha = 0.05,
  inf_frac = c(0.5, 1),
  spend_fn = "OF",
  corr = diag(x = 1, nrow = 1, ncol = 1),
  test_type = "one-sided",
  lambda = 0.1,
  tol = 1e-06,
  max_iter = 1000
)

Arguments

alpha: A numeric value specifying the overall type I error rate to control. Default is 0.05.
inf_frac: A numeric vector of information fractions indicating when analyses are conducted.
spend_fn: A character string specifying the alpha spending function. Either "OF" (O'Brien-Fleming) or "Pocock".
corr: A correlation matrix of Z-statistics at analysis time s. Should have dimension \(L \times L\).
test_type: A character string specifying the type of test to be performed. Either "one-sided" or "two-sided". Default is "one-sided".
lambda: A numeric value for the initial step size used in the iterative boundary search. Default is 0.1.
tol: A numeric value specifying the convergence tolerance. Default is 1e-6.
max_iter: A positive integer specifying the maximum number of iterations. Default is 1000.

Value

A list with the following components:

bounds / bound: A numeric value (single analysis) or numeric vector (sequential) of stopping boundaries. The key is "bounds" for a single analysis and "bound" for multiple analyses.
spending: A numeric value or vector of cumulative alpha spent at each analysis.
convergence: A logical value or vector indicating whether the algorithm converged at each analysis.
iterations: An integer value or vector of the number of iterations used at each analysis.
alpha: The input type I error rate.
inf_frac: The input information fractions.
spend_fn: The input spending function name.
corr: The input correlation matrix.
test_type: The input test type.